1. Field of the Invention
This invention relates to the field of data stream analysis.
2. Description of the Related Art
Computers are effective at capturing and processing structured information, whereas human beings are more effective at capturing the gist, or semantics in ways that are not presently (if ever) possible computationally. Yet human beings are not good at rote operations, and are not effective when forced to work long amounts of time.
Data analysis such as video analysis and categorization is an important problem in a variety of disciplines, including surveillance. Data streams such as video, audio, and sensor data streams often contain semantic information that can be useful in a variety of applications, including data mining, information retrieval and summarization. Such information is easy for human beings to detect, but hard to identify via computational methods. While it is possible to segment video streams to detect potential events against a static background, it is difficult to automate such detection activities, which can be, for a non-limiting example, identifying individuals in a crowd or to track them between different cameras with poor calibration. It is even more difficult to automate detections of social means and behaviors that have a fuzzy definition, such as “suspicious behavior” and “fighting.” Human beings, on the other hand, can be quite good at picking out important invariants in what are (to computers) quite noisy signals, and have a common sense to recognize social means and behaviors.